2027 Spring Intern - GenAI Applications

2027 Spring Intern - GenAI Applications

Why Genentech

We’re passionate about delivering on Our Promise to improve the lives of patients and create healthier communities for all. We foster a culture of inclusivity, integrity and creativity while boldly pursuing answers to the world’s most complex health challenges and transforming society.

Department Summary
Genentech’s Data, Digital, and Analytics (DDA) organization applies data science and AI to high-impact business needs across Commercial, Medical, and Government Affairs (CMG). We collaborate with business stakeholders and technology partners to translate advances in generative AI into practical applications that drive measurable business impact.

This internship position is located in South San Francisco, onsite.

The Opportunity

As an Applied GenAI Scientist Intern, you will explore how intelligent agents can reason, use specialized tools, and execute multi-step business workflows across CMG. Your project will focus on developing and evaluating a simple, reusable approach for adapting advanced GenAI agents to business use cases.

Building on the Deep Claude Agent approach, which uses Claude Code as an agent runtime, you will investigate how task instructions, reusable skills, context, and tool access affect agent performance. You will develop a working prototype and apply it to a focused CMG use case selected by your manager. Working with data scientists, business stakeholders, and platform partners, you will generate evidence and practical guidance for extending the approach to additional workflows.

Your responsibilities will include:

• Define the use case and evaluation approach. Translate a selected CMG business need into a focused agent task, research questions, and measurable success criteria.
• Design and prototype specialized GenAI agents. Combine task instructions, reusable skills, and relevant tools and data to enable an agent to carry out the selected workflow.
• Assess and de-risk the model components the agent depends on. Compare hosted and open-weight options for specialized components such as the fast, calibrated classifiers used in routing, guardrails, and scoring, weighing accuracy, latency, and cost alongside licensing and data-handling terms. Recommend self-hostable alternatives where a dependency cannot be adopted as-is.
• Conduct systematic experiments. Investigate how agent configuration, context, memory, and tool selection influence reasoning, output quality, and task completion. Analyze failure modes and identify where human review is needed.
• Evaluate business value and reliability. Compare the agent’s performance with an agreed baseline, assessing accuracy, consistency, time savings, and execution cost.
• Develop a reusable capability and share findings. Deliver a working prototype, adaptable agent configurations, and clear guidance that makes the approach easier to apply across CMG use cases. Present results, limitations, and recommendations to stakeholders.
• Bring the latest techniques from academia into our organization and apply to our use cases.

Program Highlights

• Intensive 6-months, full-time (40 hours per week), paid internship.
• Program start dates are in January 2027.
• A stipend, based on location, will be provided to help alleviate costs associated with the internship.
• Ownership of an applied GenAI project, from experimental design through demonstration and evaluation.
• Collaboration with data scientists and business experts in the biotechnology industry.

Who You Are

Required Education

You meet one of the following criteria:

• Must be pursuing a Master's Degree (enrolled student).
• Must have attained a Master's Degree.
• Must be pursuing a PhD (enrolled student).

Required Majors: Artificial Intelligence, Machine Learning, Computer Science, Data Science, Statistics, or a related quantitative discipline.

Required Skills

• Foundational understanding of machine learning and generative AI, including large language models.
• Experience using Python to conduct experiments, analyze data, or prototype AI applications through coursework, research, or personal projects.
• Ability to design experiments, critically evaluate results, and communicate findings clearly.
• Interest in applying GenAI to practical business problems and collaborating with domain experts.

Preferred Knowledge, Skills, and Qualifications

• Experience with LLM applications, AI agents, prompt design, or tool-augmented reasoning.
• Familiarity with methods for evaluating LLM accuracy, reliability, and reproducibility.
• Exposure to Claude Code, agent skills, or Model Context Protocol (MCP).
• Experience fine-tuning, serving, or benchmarking open-weight models, including encoder-based classifiers.
• Awareness of model licensing and data-handling terms when selecting third-party or open-weight components.
• Interest in commercial, medical, or government affairs applications within healthcare or biotechnology.
• Excellent communication, collaboration, and interpersonal skills.
• A commitment to Integrity, Courage, and Passion in daily work and decisions.

Relocation benefits are not available for this job posting. 

The expected salary range for this position based on the primary location of California is $52.00 per hour. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for paid holiday time off benefits.

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.